My AI agent was stagnating.
It answered questions well. It wrote code. It helped with projects. But it never got better at understanding me. Every conversation started from roughly the same baseline.
The problem was continuity. There was no mechanism for improvement.
So I built one.
Every afternoon at 3pm, my agent asks me: What worked today? What didn't?
The effect compounds. When I mention preferring concise responses, it notes that. When I say a particular workflow felt clunky, it remembers. Over weeks, those small adjustments add up to an agent that genuinely understands my preferences.
I call these scheduled feedback loops, and they changed how I use AI.
Beyond daily reflection, I have my agent check project health every 12 hours. It looks for stalled work, outdated dependencies, deployment issues. Once a week, it consolidates fragmented notes into long-term memory. Once a month, it reviews which tools I actually use and suggests better workflows.
These are prompts that make the agent proactive rather than reactive.
AI agents should compound in value over time. Every interaction should teach them something. Every mistake should refine their understanding. But that only happens if you build the feedback loops yourself.
Most people treat AI agents as static tools. You ask, they answer, nothing changes. If you schedule regular check-ins (daily reflections, weekly summaries, monthly calibrations), the agent evolves with you.
I packaged these prompts into a library called Better Claw. You can browse the full catalog of feedback loops at better-claw.vercel.app or check out the source on GitHub.
The approach is straightforward: scheduled prompts that make the agent reflect, learn, and improve.
If you're using OpenClaw (or any AI agent), try adding one feedback loop. Ask it to check in on something daily. See what happens over a few weeks.
You might be surprised how much better it gets.